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PREDICTING THE SEVERITY OF OBSTRUCTIVE SLEEP APNEA BASED ON PARANASAL SINUS COMPUTED TOMOGRAPHY SCAN USING MULTIMODAL DEEP LEARNING MODELS
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 박석원 | - |
| dc.date.accessioned | 2024-10-31T01:00:16Z | - |
| dc.date.available | 2024-10-31T01:00:16Z | - |
| dc.date.issued | 2023-04-24 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/55190 | - |
| dc.title | PREDICTING THE SEVERITY OF OBSTRUCTIVE SLEEP APNEA BASED ON PARANASAL SINUS COMPUTED TOMOGRAPHY SCAN USING MULTIMODAL DEEP LEARNING MODELS | - |
| dc.type | Conference | - |
| dc.citation.startPage | 427 | - |
| dc.citation.endPage | 427 | - |
| dc.citation.conferenceName | International Congress of ORL-HNS 2023 | - |
| dc.citation.conferencePlace | 대한민국 | - |
| dc.citation.conferencePlace | Kintex | - |
| dc.citation.conferenceDate | 2023-04-23 ~ 2023-04-25 | - |
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Related Researcher
- Park, Seok Won
- Graduate School (Department of Medicine)